Under some approaches, machine learning models may be trained by human users labeling data sets. Labeled data sets may be used by machine learning algorithms to update machine learning models in an iterative process. Model training may be accelerated by selecting data sets or examples for human scoring about which the model has the least information. Human scoring of data examples about which the model is uncertain provides more information to the model than scoring of data examples that the model already knows how to classify. Selecting uncertain examples, however, may be time consuming and resource intensive, as each labeled data example may produce model changes which produce uncertainty changes. Updating example uncertainty for each example within a large dataset may be prohibitively time-consuming and resource-intensive. These and other drawbacks exist with conventional machine learning training.